Cui Wenqiang, Aouidate Adnane, Wang Shouguo, Yu Qiuliyang, Li Yanhua, Yuan Shuguang
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.
Front Pharmacol. 2020 May 20;11:733. doi: 10.3389/fphar.2020.00733. eCollection 2020.
New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful, and promising technology for faster, cheaper, and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the different subareas of the computer-aided drug discovery process with a focus on anticancer drugs.
新药研发已被公认为是一个复杂、昂贵、耗时且具有挑战性的项目。据估计,按照传统的药物研发流程,平均研发一种新药需要约12年时间和27亿美元。如何降低新药研发的成本并加快研发进程,已成为制药行业面临的一个具有挑战性且紧迫的问题。计算机辅助药物设计(CADD)已成为一种强大且有前景的技术,可实现更快、更廉价且更有效的药物设计。近年来,用于药物研发(包括抗癌疗法)的计算工具迅速发展,已对抗癌药物设计产生了重大且显著的影响,也为癌症治疗领域提供了丰富的见解。在这项工作中,我们重点围绕抗癌药物讨论了计算机辅助药物研发过程的不同子领域。